Design and implementation of fishery information recommendation system based on association rules

Abstract: In order to obtain fishery scientific data quickly and easily, this article analyzed the user's interests in visiting fishery scientific data platforms based on data mining, mined rules and gave information recommendations according to the rules. The association rule technique, one of the commonly used algorithms for mining data analysis, attempts to find some relation of transaction items to the mass data. Based on the design of the Fisheries Information Recommendation System, the association rules mining technique was used to access the web log data of the Fishery Scientific Data Platform to find the user access pattern by data processing, pattern discovery and pattern recognition analysis procedures. Researchers analyzed and improved the algorithms involved in the mining analysis, proposed the IASR (IP Agent Session Referrer) algorithm for user identification, and introduced the technology of rewrite URL, IASR used four key informations: IP, Agent, Session and Referrer, and added session identification mechanism to recognized users in order to improve the accuracy of user identification. In the light of algorithm research on association rules, the paper proposed an improved Apriori algorithm for solving the problems of a large number of candidate set data generated in the connected computing in the process, by judging the effectiveness of the joint operation in advance to reduce the item sets joint operations, the number of the candidate item sets and iterative operations for increasing the efficiency of computation. Comparative testing was made including IASR, Apriori algorithm and improved Apriori algorithm by using the web logging data of the Fisheries Science Data Platform as the experimental object. The result of experimental research showed that IASR algorithm can improve the accuracy of user identification by 13%, and that the speed is twice as fast as that of the traditional one. The improved Apriori algorithm can greatly improve the calculation efficiency. When the transaction number is greater than 500, the improved algorithm efficiency is much better than the Apriori algorithm, improving the speed by more than 6 times.The recommendation system was designed based on the research. The overall structure of the system was designed into three layers: the service layer, business logic layer and data layer, while the service module was encapsulated in the business logic layer. Then registered users and non - registered users were provided needed information recommendation services respectively. The fishery information recommendation system was developed by using JAVA, Asynchronous JavaScript and XML, SQL Server2005 and Windows XP. The application results showed that the implementation of the system could improve the quality of information service and allow users to gain the fishery information they are interested in quickly and easily.